S. Gribling

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20242026

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math.OC2026

Squared polynomial approximation kernels for the hypercube: improved error bounds and implications for Lasserre hierarchies

Sander Gribling, Etienne de Klerk, Juan C. Vera

We propose a new family of polynomial approximation kernels for approximating nonnegative polynomials on the hypercube $[- 1, 1]^{n}$ . Our Kernels produce polynomial sums-of-squares of…

math.OC2025

Revisiting the convergence rate of the Lasserre hierarchy for polynomial optimization over the hypercube

Sander Gribling, Etienne de Klerk, Juan Vera

We revisit the problem of minimizing a given polynomial $f$ on the hypercube $[- 1, 1]^{n}$ . Lasserre's hierarchy (also known as the moment- or sum-of-squares hierarchy) provides a seq…

math.OC2024

Mutually unbiased bases: polynomial optimization and symmetry

Sander Gribling, Sven Polak

A set of $k$ orthonormal bases of $C^{d}$ is called mutually unbiased if $∣ ⟨ e, f ⟩ ∣^{2} = 1/ d$ whenever $e$ and $f$ are basis vectors in distinct bases. A natural q…